Shadi Aslebagh

Master’s Thesis Defense

Nov. 28, 2012

University of Central Florida

To develop an oceanic Rain Accumulation (RA) data

product in support of NASA’s Aquarius (AQ) Sea

Surface Salinity (SSS) measurements

◦ This auxiliary science product will enable scientist and

AQ remote sensing engineers to examine the influence of

oceanic rainfall on the retrieval of SSS

Undetected rainfall can produce “false signals” that create

errors in the AQ SSS retrieval

The use of this RA product will provide independent science

data that may enable algorithm developers to mitigate the

effects of rain from the SSS retrieval

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A joint science program by NASA and the Argentine

Space Agency (CONAE)

The main objective of this mission is to provide the

monthly global maps of Sea Surface Salinity (SSS)

SSS

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The prime microwave

remote sensor

Passive Microwave

◦ Dicke radiometer

operating @ 1.41 GHz

Active Microwave

◦ Scatterometer operating

@ 1.26 GHz

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A sun-synchronous polar

orbit satellite

Flies in a terminator orbit:

AQ collects blackbody

emissions from the night

side of ground track

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Three beams

EIA = 28.7o, 37.8o

and 45.6o

IFOV = 79×94, 84×120

and 96×156 Km

SSS Retrieval Algorithm

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×

SSS Retrieval Algorithm

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Real WorldImpact of Environmental Parameters on SSS

Retrieval

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psu

Rain Rate

Instantaneous Rain Rate – roughness effect

Rain Accumulation – layer of fresh water floating

on sea water

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Instantaneous Rain effect on AQ SSS:

◦ Instantaneous rain (splash effect) will cause increased

roughness that raises the ocean surface brightness

temperature

This decreases the retrieved SSS

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Longer-term rain effect on

AQ SSS:

◦ Layered media effect that can

affect the ocean Tb

This electromagnetic impedance

matching effect can either

increase or decrease ε depending

upon l

Thus the apparent (retrieved)

SSS may increase or decrease

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air

Fresh Water

Sea Water

Transmitted emission

is a measure of the efficiency of

transmission of internal blackbody

radiation across the air/sea interface

black body

emission

internal reflected

emission

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𝑍23 = 𝜂2(𝜂3 cos 𝑘2𝑙 + 𝑗𝜂2sin(𝑘2𝑙)

𝜂2 cos 𝑘2𝑙 + 𝑗𝜂3sin(𝑘2𝑙))

𝜌 =𝑍23 − 𝜂1𝑍23 + 𝜂1

Γ = 𝜌 2

𝑒𝑚𝑖𝑠𝑠𝑖𝑣𝑖𝑡𝑦 = 1 − Γ

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Decre

ase A

QSSS

Incre

ase A

QSSS

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Decre

ase A

QSSS

Incre

ase A

QSSS

Use independent global 3-hr ocean rain rate

measurements (TRMM 3B42 product)

◦ Calculate the accumulated rain rate for each AQ footprint as a

function of time before the AQ observation

Instantaneous rain rate @ observation time (To)

RA: Rain accumulations for To – 3, To – 6, To – 9, . . . To – 24

the output to an "Overlay file“

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AQ Level-2

Produced by NASA/GSFC

Available through PODAAC at NASA JPL

Takes two weeks for files availability ◦ AQ orbit#

◦ AQ cycle#

◦ AQ IFOV center location

◦ AQ observation time

◦ AQ flags

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TRMM 3B42

Accessible through NASA

GES

Takes one month files

availability

◦ Every 3 hours snapshot

◦ Earth gridded 0.25o×0.25o

◦ Covers ±50 Lat. and ±180 Lng.

◦ Merged MW and IR

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RA: is defined as the total amount of rainfall which

accumulates over each 0.25o×0.25o box during the

whole ΔT time prior to the observation time.

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Determine the matrix

elements that fall within

the AQ IFOV

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Perform time interpolation of 3B42 to the AQ

observation time

◦ 3B42 every 3 hours: 00,03, 06, 09, 12, 15, 18 and 21

GMT

◦ Pick the 3B42 file which has the closest time to the AQ

observation time for rain rate at AQ observation time (T0)

◦ Retreat in time and pick 3B42 files for 3, 6, 9, 12, 15, 18

and 21hr prior to T0 for previous rain rates

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Linear time interpolation of 3B42 in 0.25 hr

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Calculate rain accumulation for 3 hour windows

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RA is a new product → no “truth” value available for

validations

To validate interpolation

To Validate the product: Use WindSat EDR rain rate and

collocate with interpolated 3B42 rain rate at AQ

observation time (TBD)

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An algorithm has been developed to estimate the RA

in a 3-hr window over the previous 24-hrs over the

AQ IFOV’s

This RA product is important to AQ science

objectives to improve SSS measurements.

Based upon preliminary comparison of independent

satellite rain measurements from the WindSat

radiometer, we believe that the RA products are good

approximations to the “true rain accumulations” over

the AQ IFOV’s.

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A statistical analysis should be performed to develop

an empirical relationship between the RA and

observed errors in the retrieved SSS.

An electromagnetic model should be established to

calculate the AQ Tb for a lens thickness equal to RA

over the oceans.

Since diffusion of salt into fresh water lens caused by

rain fall over oceans and mechanical mixing restores

the ambient SSS in time, an independent study should

be performed to model this procedure.

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S. Aslebagh, Y. Hejazin, L. Jones, C. May and R. Gonzalez, An Oceanic Rain Flag for Aquarius, Oceans’12, Oct. 2012

Y. Hejazin, S. Aslebagh and L. Jones, Aquarius/SAC-D MicroWave Radiometer Ocean Wind Speed Measurements, Oceans’12, Oct. 2012

S. Aslebagh and L. Jones, TMI-WindSat Double Difference XCAL Tb Bias, XCAL Science Team Meeting, Jul. 2012

A. S. Garcia, S. Aslebagh, S. Biswas, L. Jones, M. Labanda and M. Marta Jacob, SAC-D MWR Brightness Temperature Inter-satellite Radiometric Calibration, Aquarius Science Team Meeting, Apr. 2012

L. Jones, S. Bilanow, S. Farrar, S. Aslebagh, The Effect of TMI 1B11 V7 Solar Beta/Time Varying Bias Correction on 2A12 Rain Rate, XCAL Science Team Meeting, Nov. 2011

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Questions?